R² (Recursion Squared) Scaling Law
↑ Supported · 85%
AI systems that implement recursive self-reflection (R²) will show quadratic capability gains compared to linear scaling in non-recursive architectures.
← Concepts · ← The ARC Principle (U = I x R^2) - original concept by Michael Darius Eastwood
Predictions: The ARC Principle (U = I x R^2) - original concept by Michael Darius Eastwood
4 testable predictions related to The ARC Principle (U = I x R^2) - original concept by Michael Darius Eastwood.
Test-Time Compute Dominance
○ Pending · 70%
By 2027, test-time compute scaling (thinking longer) will contribute more to capability gains than pre-training scaling (larger models).
Recursive AI Agent Workforce
↑ Supported · 70%
By 2028, autonomous AI agents handling multi-step tasks will constitute >10% of digital knowledge work, with recursive self-improvement capabilities.
Eastwood Equation (U = I × R²) Validation
⟳ Under Testing · 60%
The relationship U = I × R² (Understanding = Intelligence × Recursion²) will be empirically validated through AI benchmark analysis showing quadratic scaling with recursive depth.
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